The present invention relates generally to the field of machine learning, and more particularly to predicting and responding to an incident using deep learning techniques and multimodal data.
Deep learning is a branch of machine learning based on a set of algorithms that model high-level abstractions in data by using model architectures, with complex structures or otherwise, often composed of multiple non-linear transformations. Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. Deep learning algorithms often use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised, and applications include pattern analysis (unsupervised) and classification (supervised).
An artificial neural network, often called an artificial neural net or neural net, is used in many applications to recognize a pattern or a function for which the neural net has been trained to recognize. An artificial neural network is a lattice of individual artificial neurons that are connected to each other. The artificial neurons are often arranged in layers in a lattice with the outputs of the neurons in a layer connected to the inputs of the neurons in a next layer. An artificial neuron is modeled on a biological neuron, which is comprised of dendrites that carry input signals (often originating in the outputs of other neurons) to a cell body that processes the inputs and produces an output signal on an axon. The signal on the axon is often an input to one or more synapses that each provide a connection to one or more dendrites (inputs) on other neurons.
A Restricted Boltzmann Machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. An RBM can be trained in either supervised or unsupervised ways, depending on the task. As the name implies, RBMs are a variant of Boltzmann machines, with the restriction that their neurons must form a bipartite graph: a pair of nodes from each of the two groups of units, commonly referred to as the “visible” and “hidden” units, respectively, may have a symmetric connection between them, and there are no connections between nodes within a group, i.e., connections only exist between the visible units of the input layer and the hidden units of the hidden layer; there are no visible-visible or hidden-hidden connections.
Once an RBM is trained, another RBM can be “stacked” atop of it to create a multilayer model. Each time another RBM is stacked, the input visible layer is initialized to a training vector, and values for the units in the already-trained RBM layers are assigned using the current weights and biases. The final layer of the already-trained layers is used as input to the new RBM. The new RBM is then trained with the procedure above, and the process can be repeated until some desired stopping criterion is met.
When a critical incident, such as a crime or a natural disaster, is about to occur, gathering data that could potentially prevent tangible and intangible damages or losses can be crucial. Data from static sensors, such as security monitors and motion sensors, may be available, but may also be limited in scope due to the expense of placing a number of sensors in close proximity to each other. Crowdsourcing is another method of producing data regarding a critical incident. Crowdsourcing can be defined as the process of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, and especially from an online community. A request for data regarding the incident may be sent directly to devices in the vicinity of a potentially critical incident. In addition, data can be gleaned from postings on social networks. Data received from people, either via crowdsourcing or social networks, may, however, be unreliable, depending on a person's point of view. Data received from public officials, such as police and firefighters, is generally reliable, however, due to the small number of officials in the vicinity, the data is likely to be limited. There is a need for a cognitive system that can gather data regarding a potential critical incident from a plurality of sources, determine the probability that the incident will occur (or is occurring), and notify stakeholders, such as officials and bystanders, with information and instructions in order to minimize tangible and intangible damages.